混合可観測領域におけるマルチアームバンディット緩和による最適センシング
Optimal Sensing via Multi-armed Bandit Relaxations in Mixed Observability Domains
部分的に観測可能な確率過程の情報量を最大化するセンシング問題を、制約を緩和してマルチアームバンディットに帰着し、得られた上界で探索空間を枝刈りする手法を提案した。
著者: Mikko Lauri, Risto Ritala
分類: cs.AI, cs.RO, cs.SY, eess.SY
原文アブストラクト
Sequential decision making under uncertainty is studied in a mixed observability domain. The goal is to maximize the amount of information obtained on a partially observable stochastic process under constraints imposed by a fully observable internal state. An upper bound for the optimal value function is derived by relaxing constraints. We identify conditions under which the relaxed problem is a multi-armed bandit whose optimal policy is easily computable. The upper bound is applied to prune the search space in the original problem, and the effect on solution quality is assessed via simulation experiments. Empirical results show effective pruning of the search space in a target monitoring domain.
